Multiple phase flow identification using computational simulation and convolutional neural network

The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion...

全面介紹

Saved in:

書目詳細資料
主要作者:	Helmy, Mohamed Tawfik Ibrahim
格式:	Thesis
語言:	English
出版:	2020
主題:	TK Electrical engineering Electronics Nuclear engineering
在線閱讀:	http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

id	my-utm-ep.93119
record_format	uketd_dc
spelling	my-utm-ep.931192021-11-19T03:31:23Z Multiple phase flow identification using computational simulation and convolutional neural network 2020 Helmy, Mohamed Tawfik Ibrahim TK Electrical engineering. Electronics Nuclear engineering The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion of the gas-solid two-phase flow in densephase usually has a nonlinear and unsteady nature that needs to be examined and analysed to identify the particle flow behaviour in the pneumatic conveying pipelines. In this research a method to identify the type of flow pattern is proposed using a computational method where a gravity flow rig is modelled on Solidworks and multiple flow patterns are simulated with different mass flow rates ranging between 200 to 600 g/s. For changing the flow patterns inside the pipe, an Iris Mechanism is designed according to the specifications of the flow required to achieve the flow pattern control. A sectioning method is implemented to capture flow images at the plane of interest for different flow patterns. Afterwards images are fed to a Convolutional Neural Network which is trained and tested to identify the flowpatterns according to several flowfeatures which resulted in 100% accuracy. A GUI using PyQt is designed to better visualize the whole system and view the predicted flow pattern. 2020 Thesis http://eprints.utm.my/id/eprint/93119/ http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135980 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Helmy, Mohamed Tawfik Ibrahim Multiple phase flow identification using computational simulation and convolutional neural network
description	The Identification of gas-solid flow characterization in dense-phase pneumatic conveying particles is very important to a vast area of industrial fields such as chemical and pharmaceutical industries since a slight change in flow characteristics results in a completely different product. The motion of the gas-solid two-phase flow in densephase usually has a nonlinear and unsteady nature that needs to be examined and analysed to identify the particle flow behaviour in the pneumatic conveying pipelines. In this research a method to identify the type of flow pattern is proposed using a computational method where a gravity flow rig is modelled on Solidworks and multiple flow patterns are simulated with different mass flow rates ranging between 200 to 600 g/s. For changing the flow patterns inside the pipe, an Iris Mechanism is designed according to the specifications of the flow required to achieve the flow pattern control. A sectioning method is implemented to capture flow images at the plane of interest for different flow patterns. Afterwards images are fed to a Convolutional Neural Network which is trained and tested to identify the flowpatterns according to several flowfeatures which resulted in 100% accuracy. A GUI using PyQt is designed to better visualize the whole system and view the predicted flow pattern.
format	Thesis
qualification_level	Master's degree
author	Helmy, Mohamed Tawfik Ibrahim
author_facet	Helmy, Mohamed Tawfik Ibrahim
author_sort	Helmy, Mohamed Tawfik Ibrahim
title	Multiple phase flow identification using computational simulation and convolutional neural network
title_short	Multiple phase flow identification using computational simulation and convolutional neural network
title_full	Multiple phase flow identification using computational simulation and convolutional neural network
title_fullStr	Multiple phase flow identification using computational simulation and convolutional neural network
title_full_unstemmed	Multiple phase flow identification using computational simulation and convolutional neural network
title_sort	multiple phase flow identification using computational simulation and convolutional neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93119/1/MohamedTawfikIbrahimMSKE2020.pdf
_version_	1747818635083120640

Multiple phase flow identification using computational simulation and convolutional neural network

相似書籍